Statistical Learning And Modeling In Data Analysis

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An Introduction to Statistical Learning

Author : Gareth James
Publisher : Springer Nature
ISBN 13 : 3031387473
Total Pages : 617 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis An Introduction to Statistical Learning by : Gareth James

Download or read book An Introduction to Statistical Learning written by Gareth James and published by Springer Nature. This book was released on 2023-08-01 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

Statistical Learning and Modeling in Data Analysis

Author : Simona Balzano
Publisher : Springer
ISBN 13 : 9783030699437
Total Pages : 182 pages
Book Rating : 4.6/5 (994 download)

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Book Synopsis Statistical Learning and Modeling in Data Analysis by : Simona Balzano

Download or read book Statistical Learning and Modeling in Data Analysis written by Simona Balzano and published by Springer. This book was released on 2021-07-14 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: The contributions gathered in this book focus on modern methods for statistical learning and modeling in data analysis and present a series of engaging real-world applications. The book covers numerous research topics, ranging from statistical inference and modeling to clustering and factorial methods, from directional data analysis to time series analysis and small area estimation. The applications reflect new analyses in a variety of fields, including medicine, finance, engineering, marketing and cyber risk. The book gathers selected and peer-reviewed contributions presented at the 12th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2019), held in Cassino, Italy, on September 11–13, 2019. CLADAG promotes advanced methodological research in multivariate statistics with a special focus on data analysis and classification, and supports the exchange and dissemination of ideas, methodological concepts, numerical methods, algorithms, and computational and applied results. This book, true to CLADAG’s goals, is intended for researchers and practitioners who are interested in the latest developments and applications in the field of data analysis and classification.

Statistical Learning and Modeling in Data Analysis

Author : Simona Balzano
Publisher : Springer Nature
ISBN 13 : 3030699447
Total Pages : 182 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Statistical Learning and Modeling in Data Analysis by : Simona Balzano

Download or read book Statistical Learning and Modeling in Data Analysis written by Simona Balzano and published by Springer Nature. This book was released on 2021-07-13 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: The contributions gathered in this book focus on modern methods for statistical learning and modeling in data analysis and present a series of engaging real-world applications. The book covers numerous research topics, ranging from statistical inference and modeling to clustering and factorial methods, from directional data analysis to time series analysis and small area estimation. The applications reflect new analyses in a variety of fields, including medicine, finance, engineering, marketing and cyber risk. The book gathers selected and peer-reviewed contributions presented at the 12th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2019), held in Cassino, Italy, on September 11–13, 2019. CLADAG promotes advanced methodological research in multivariate statistics with a special focus on data analysis and classification, and supports the exchange and dissemination of ideas, methodological concepts, numerical methods, algorithms, and computational and applied results. This book, true to CLADAG’s goals, is intended for researchers and practitioners who are interested in the latest developments and applications in the field of data analysis and classification.

Statistical and Machine-Learning Data Mining

Author : Bruce Ratner
Publisher : CRC Press
ISBN 13 : 1466551216
Total Pages : 544 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis Statistical and Machine-Learning Data Mining by : Bruce Ratner

Download or read book Statistical and Machine-Learning Data Mining written by Bruce Ratner and published by CRC Press. This book was released on 2012-02-28 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has completely revised, reorganized, and repositioned the original chapters and produced 14 new chapters of creative and useful machine-learning data mining techniques. In sum, the 31 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. The statistical data mining methods effectively consider big data for identifying structures (variables) with the appropriate predictive power in order to yield reliable and robust large-scale statistical models and analyses. In contrast, the author's own GenIQ Model provides machine-learning solutions to common and virtually unapproachable statistical problems. GenIQ makes this possible — its utilitarian data mining features start where statistical data mining stops. This book contains essays offering detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. They address each methodology and assign its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.

Statistical Learning of Complex Data

Author : Francesca Greselin
Publisher : Springer Nature
ISBN 13 : 3030211401
Total Pages : 201 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Statistical Learning of Complex Data by : Francesca Greselin

Download or read book Statistical Learning of Complex Data written by Francesca Greselin and published by Springer Nature. This book was released on 2019-09-06 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book of peer-reviewed contributions presents the latest findings in classification, statistical learning, data analysis and related areas, including supervised and unsupervised classification, clustering, statistical analysis of mixed-type data, big data analysis, statistical modeling, graphical models and social networks. It covers both methodological aspects as well as applications to a wide range of fields such as economics, architecture, medicine, data management, consumer behavior and the gender gap. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field of data analysis and classification. It gathers selected and peer-reviewed contributions presented at the 11th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2017), held in Milan, Italy, on September 13–15, 2017.

Statistical Foundations of Data Science

Author : Jianqing Fan
Publisher : CRC Press
ISBN 13 : 0429527616
Total Pages : 942 pages
Book Rating : 4.4/5 (295 download)

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Book Synopsis Statistical Foundations of Data Science by : Jianqing Fan

Download or read book Statistical Foundations of Data Science written by Jianqing Fan and published by CRC Press. This book was released on 2020-09-21 with total page 942 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.

Statistical and Machine-Learning Data Mining:

Author : Bruce Ratner
Publisher : CRC Press
ISBN 13 : 149879761X
Total Pages : 690 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Statistical and Machine-Learning Data Mining: by : Bruce Ratner

Download or read book Statistical and Machine-Learning Data Mining: written by Bruce Ratner and published by CRC Press. This book was released on 2017-07-12 with total page 690 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. What is new in the Third Edition: The current chapters have been completely rewritten. The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops. Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing (NLP). Includes SAS subroutines which can be easily converted to other languages. As in the previous edition, this book offers detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. The author addresses each methodology and assigns its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.

The Elements of Statistical Learning

Author : Trevor Hastie
Publisher : Springer Science & Business Media
ISBN 13 : 0387216065
Total Pages : 545 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis The Elements of Statistical Learning by : Trevor Hastie

Download or read book The Elements of Statistical Learning written by Trevor Hastie and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

Statistical Data Modeling and Machine Learning with Applications

Author : Snezhana Gocheva-Ilieva
Publisher : Mdpi AG
ISBN 13 : 9783036526928
Total Pages : 184 pages
Book Rating : 4.5/5 (269 download)

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Book Synopsis Statistical Data Modeling and Machine Learning with Applications by : Snezhana Gocheva-Ilieva

Download or read book Statistical Data Modeling and Machine Learning with Applications written by Snezhana Gocheva-Ilieva and published by Mdpi AG. This book was released on 2021-12-21 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section "Mathematics and Computer Science". Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties.

Statistical Modeling and Analysis for Database Marketing

Author : Bruce Ratner
Publisher : CRC Press
ISBN 13 : 0203496906
Total Pages : 383 pages
Book Rating : 4.2/5 (34 download)

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Book Synopsis Statistical Modeling and Analysis for Database Marketing by : Bruce Ratner

Download or read book Statistical Modeling and Analysis for Database Marketing written by Bruce Ratner and published by CRC Press. This book was released on 2003-05-28 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traditional statistical methods are limited in their ability to meet the modern challenge of mining large amounts of data. Data miners, analysts, and statisticians are searching for innovative new data mining techniques with greater predictive power, an attribute critical for reliable models and analyses. Statistical Modeling and Analysis fo

Data Science and Machine Learning

Author : Dirk P. Kroese
Publisher : CRC Press
ISBN 13 : 1000730778
Total Pages : 538 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Data Science and Machine Learning by : Dirk P. Kroese

Download or read book Data Science and Machine Learning written by Dirk P. Kroese and published by CRC Press. This book was released on 2019-11-20 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code

Advances in Statistical Models for Data Analysis

Author : Isabella Morlini
Publisher : Springer
ISBN 13 : 3319173774
Total Pages : 264 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Advances in Statistical Models for Data Analysis by : Isabella Morlini

Download or read book Advances in Statistical Models for Data Analysis written by Isabella Morlini and published by Springer. This book was released on 2015-09-04 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited volume focuses on recent research results in classification, multivariate statistics and machine learning and highlights advances in statistical models for data analysis. The volume provides both methodological developments and contributions to a wide range of application areas such as economics, marketing, education, social sciences and environment. The papers in this volume were first presented at the 9th biannual meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, held in September 2013 at the University of Modena and Reggio Emilia, Italy.

Advanced Linear Modeling

Author : Ronald Christensen
Publisher : Springer Nature
ISBN 13 : 3030291642
Total Pages : 618 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Advanced Linear Modeling by : Ronald Christensen

Download or read book Advanced Linear Modeling written by Ronald Christensen and published by Springer Nature. This book was released on 2019-12-20 with total page 618 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces several topics related to linear model theory, including: multivariate linear models, discriminant analysis, principal components, factor analysis, time series in both the frequency and time domains, and spatial data analysis. This second edition adds new material on nonparametric regression, response surface maximization, and longitudinal models. The book provides a unified approach to these disparate subjects and serves as a self-contained companion volume to the author's Plane Answers to Complex Questions: The Theory of Linear Models. Ronald Christensen is Professor of Statistics at the University of New Mexico. He is well known for his work on the theory and application of linear models having linear structure.

Doing Data Science

Author : Cathy O'Neil
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 144936389X
Total Pages : 408 pages
Book Rating : 4.4/5 (493 download)

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Book Synopsis Doing Data Science by : Cathy O'Neil

Download or read book Doing Data Science written by Cathy O'Neil and published by "O'Reilly Media, Inc.". This book was released on 2013-10-09 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

Statistical and Machine-Learning Data Mining

Author : Bruce Ratner
Publisher : CRC Press
ISBN 13 : 1466551216
Total Pages : 544 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis Statistical and Machine-Learning Data Mining by : Bruce Ratner

An Introduction to Statistical Learning

Author : Gareth James
Publisher : Springer Nature
ISBN 13 : 1071614185
Total Pages : 607 pages
Book Rating : 4.0/5 (716 download)

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Book Synopsis An Introduction to Statistical Learning by : Gareth James

Download or read book An Introduction to Statistical Learning written by Gareth James and published by Springer Nature. This book was released on 2021-07-29 with total page 607 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

Advanced R Statistical Programming and Data Models

Author : Matt Wiley
Publisher : Apress
ISBN 13 : 1484228723
Total Pages : 649 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Advanced R Statistical Programming and Data Models by : Matt Wiley

Download or read book Advanced R Statistical Programming and Data Models written by Matt Wiley and published by Apress. This book was released on 2019-02-20 with total page 649 pages. Available in PDF, EPUB and Kindle. Book excerpt: Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes multiple examples using R to achieve results, and concludes with a case study. Written by Matt and Joshua F. Wiley, Advanced R Statistical Programming and Data Models shows you how to conduct data analysis using the popular R language. You’ll delve into the preconditions or hypothesis for various statistical tests and techniques and work through concrete examples using R for a variety of these next-level analytics. This is a must-have guide and reference on using and programming with the R language. What You’ll LearnConduct advanced analyses in R including: generalized linear models, generalized additive models, mixed effects models, machine learning, and parallel processing Carry out regression modeling using R data visualization, linear and advanced regression, additive models, survival / time to event analysis Handle machine learning using R including parallel processing, dimension reduction, and feature selection and classification Address missing data using multiple imputation in R Work on factor analysis, generalized linear mixed models, and modeling intraindividual variability Who This Book Is For Working professionals, researchers, or students who are familiar with R and basic statistical techniques such as linear regression and who want to learn how to use R to perform more advanced analytics. Particularly, researchers and data analysts in the social sciences may benefit from these techniques. Additionally, analysts who need parallel processing to speed up analytics are given proven code to reduce time to result(s).